df_segments_length <- data.frame()
df_segments_length_tm <- dbGetQuery(con, "SELECT session_id, SUM(length) as distance, travelmode
FROM danielasocas.segments_travelmode as segments
WHERE segments.travelmode = 'car' OR segments.travelmode = 'bus'
GROUP BY session_id, travelmode")
df_segments_length <- dbGetQuery(con, "SELECT session_id, SUM(length) as distance
FROM danielasocas.segments_travelmode as segments
WHERE segments.travelmode = 'car' OR segments.travelmode = 'bus'
GROUP BY session_id")
df_segments <- subset(df_segments_length, distance < IQR(distance,0.75) )
ggplot(df_segments, aes(distance)) +
geom_histogram(bins = 10)
ggplot(df_segments_length, aes(travelmode, distance, color = travelmode)) +
geom_boxplot()
ggplot(df_segments, aes(distance, color = travelmode)) +
geom_histogram(binwidth = 1)
ggplot(df_segments, aes(travelmode, distance, color = travelmode)) +
geom_boxplot()
df_segments_timestamp <- data.frame()
df_segments_timestamp <- dbGetQuery(con, "SELECT session_id, time_start,time_end, length, travelmode
FROM danielasocas.segments_travelmode as segments
WHERE segments.travelmode = 'car' OR segments.travelmode = 'bus'")
Getting the points filtered by only segments with travelmode = car or bus.
Getting the sessions filtered by only segments with travelmode = car or bus.
df_sessions_edge_osm_cars <- data.frame()
df_sessions_edge_osm_cars <- dbGetQuery(con, "SELECT way_id, COUNT(DISTINCT session_id) AS sessions
FROM sensemyfeup_raw.osmlocation as osm
INNER JOIN danielasocas.segments_travelmode as segments
USING (session_id)
INNER JOIN sensemyfeup_raw.session_offsets
USING (session_id)
WHERE (osm.seconds + session_offsets.clock_offset_seconds BETWEEN segments.seconds_start AND seconds_end) AND
segments.travelmode = 'car' OR segments.travelmode = 'bus'
GROUP BY way_id
ORDER BY sessions DESC")
save(df_sessions_edge_osm_cars, file="df_sessions_edge_osm_cars.Rda")
p1 <- subset(df_osm_edge, points < mean(points))
summary()
ggplot(p1, aes(points)) + geom_histogram(binwidth = 10) +
xlim(0,1000)
p1 <- subset(df_osm_edge, sessions < mean(sessions))
summary()
ggplot(p1, aes(sessions)) + geom_histogram(binwidth=1) +
scale_x_continuous(breaks = seq(0,8,1))
list_osm_edge <- df_osm_edge[, 1]
fun.ecdf <- ecdf(df_osm_edge$sessions)
my.ecdf <- fun.ecdf(sort(df_osm_edge$sessions))
my_ecdf_df <- data.frame(my.ecdf)
my_ecdf_df$sessions <- sort(df_osm_edge$sessions)
tail(my_ecdf_df, 400)
P <- ecdf(df_osm_edge$sessions)
plot(P, log="x", xlim=c(1, max(df_osm_edge$sessions)))
# Showing Map.
m